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ultralytics 8.3.71 require explicit torch.nn usage (#19067)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: RizwanMunawar <chr043416@gmail.com>
Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2025-02-05 01:08:17 +09:00 • committed by GitHub
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ultralytics/models/sam/modules/sam.py
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@ -426,8 +426,7 @@ class SAM2Model(torch.nn.Module):
high_res_masks: Tensor of shape (B, 1, H*16, W*16) with the best high-resolution mask.
obj_ptr: Tensor of shape (B, C) with object pointer vector for the output mask.
object_score_logits: Tensor of shape (B) with object score logits.
Where M is 3 if multimask_output=True, and 1 if multimask_output=False.
Where M is 3 if multimask_output=True, and 1 if multimask_output=False.
Examples:
>>> backbone_features = torch.rand(1, 256, 32, 32)